Daily groundwater level is an indicator of resources. Accurate and reliable (GWL) prediction crucial for resources management land subsidence risk assessment. In this study, a representative deep learning model, long short-term memory (LSTM), adopted to predict with the selected predictors by partial mutual information (PMI), bootstrap employed generate different samples combination training ma...

Micro-Electric Discharge Machining (μ-EDM) is one of the widely applied micromanufacturing processes. However, it has several limitations, such as a low cutting rate, difficult debris removal, and poor surface integrity, etc. Hybridization μ-EDM proposed an alternative to overcome process limitations. Conversely, complicates nature poses challenge for modelling predicting critical responses. Th...

Across the world, healthcare systems are under stress and this has been hugely exacerbated by COVID pandemic. Key Performance Indicators (KPIs), usually in form of time-series data, used to help manage that stress. Making reliable predictions these indicators, particularly for emergency departments (ED), can facilitate acute unit planning, enhance quality care optimise resources. This motivates...

The complexity and changefulness of inland navigation environment in space time makes it hard to guarantee the applicability accuracy existing ship speed models. In this paper, a novel method for modelling under complex changeful is proposed. Firstly, an unsupervised machine learning algorithm, Density-Based Spatial Clustering Application with Noise (DBSCAN), utilized cluster environmental data...

In this paper we present an efficient system for independent speaker speech recognition based on neural network approach. The proposed architecture comprises two phases: a preprocessing phase which consists in segmental normalization and features extraction and a classification phase which uses neural networks based on nonparametric density estimation namely the general regression neural networ...

Modeling NOx emissions from coal fired utility boiler is critical to develop a predictive emissions monitoring system (PEMS) and to implement combustion optimization software package for low NOx combustion. This paper presents an efficient NOx emissions model based on support vector regression (SVR), and compares its performance with traditional modeling techniques, i.e., back propagation (BPNN...

In order to improve the accuracy of synthetic aperture radar images target recognition, we have proposed a new algorithm of SAR target recognition based on advance Deep Learning neural network. The traditional radar recognition algorithm has many disadvantages, In order to improve the accuracy of synthetic aperture radar images target recognition, the author have proposed a new algorithm of SAR...

The main purpose of this paper is to apply the Fuzzy based General Regression Neural Network (FGRNN) to the speed control of induction motor. A General Regression Neural Network (GRNN) is adopted to estimate the motor speed and thus provide a sensorless speed estimator system. The performance of the proposed FGRNN speed controller is evaluated for a wide range of operating conditions for induct...

Keywords: Demand forecasting Genetic algorithm–simulated annealing (GA–SA) Support vector regression (SVR) Autoregressive integrated moving average (ARIMA) General regression neural networks (GRNN) Third generation (3G) mobile phone a b s t r a c t Taiwan is one of the countries with higher mobile phone penetration rate in the world, along with the increasing maturity of 3G relevant products, t...

Abstract This paper presents a neural-based model for estimating the particle size distribution (PSD) of a polymer latex, which is an important physical characteristic that determines some end-use properties of the material (e.g., when it is used as an adhesive, a coating, or an ink). The PSD of a dilute latex is estimated from combined DLS (dynamic light scattering) and ELS (elastic light scat...